Data sharing in neuroimaging research

Neurospin, Commissariat à l'Energie Atomique et aux Energies Alternatives Gif-sur-Yvette, France.
Frontiers in Neuroinformatics (Impact Factor: 3.26). 04/2012; 6:9. DOI: 10.3389/fninf.2012.00009
Source: PubMed


Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

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Available from: Jean-Baptiste Poline
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    • "Sharing data within a larger community is a practical way to address these issues with greater statistical power and propose scientific questions beyond the scope of a single research group. Furthermore, the robustness of biological findings across different methods or processing architectures encourages confidence in the reproducibility of results, a fundamental requirement of good scientific practice (Glatard et al., 2015; Poline et al., 2012). At the same time, cross-site data sharing brings with it a broad range of issues in terms of site/scanner compatibility (Jovicich et al. 2009, 2013, 2014) and the logistical challenges of IT interoperability. "
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